{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b48c1cd-e553-494f-b168-2e8096ee7fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-03 12:53:17.257474: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "\n",
    "import diffrax\n",
    "import flax\n",
    "import IPython.display as ipd\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import optax\n",
    "import seaborn as sns\n",
    "from score_flow import sde_lib\n",
    "import tensorflow_probability.substrates.jax as tfp\n",
    "\n",
    "import probability_flow\n",
    "from posterior_sampling import realnvp_model\n",
    "from posterior_sampling import model_utils as mutils\n",
    "\n",
    "sns.set(font='serif', font_scale=1.5)\n",
    "tfd = tfp.distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0301e872-de24-4596-bbad-777a4f056e28",
   "metadata": {},
   "source": [
    "# Posterior Sampling with Score-based Priors: 2D Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0130103e-2df7-45ac-8025-bee8c377edfc",
   "metadata": {},
   "source": [
    "## Ground-truth posterior\n",
    "\n",
    "In this example, we will work with a mixture-of-Gaussians prior and a linear forward model. The resulting posterior is also a mixture-of-Gaussians.\n",
    "\n",
    "**Prior:** $\\mathbf{x}\\sim\\sum_{k=1}^K\\mathcal{N}(\\mathbf{\\mu}_k,\\beta_k^2\\mathbf{I}_2)$\n",
    "\n",
    "**Likelihood:** $y=\\mathbf{a^\\intercal x}+\\eta,\\quad\\eta\\sim\\mathcal{N}(0,\\sigma^2)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c7e281bb-ae3d-4f88-8d18-1e5db8211f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The prior is a mixture-of-Gaussians with K Gaussian components.\n",
    "Each component has a weight, mean, and diagonal covariance with uniform scale.\n",
    "You can change these parameters to see how the posterior changes.\n",
    "\"\"\"\n",
    "means = np.array(\n",
    "    [[-0.5, -0.5],\n",
    "    [0.5, 0.5]]\n",
    ").astype(np.float32)\n",
    "betas = np.array([0.1, 0.1], dtype=np.float32)\n",
    "weights = np.array([0.5, 0.5], dtype=np.float32)\n",
    "K = len(weights)\n",
    "prior = tfd.MixtureSameFamily(\n",
    "    mixture_distribution=tfd.Categorical(probs=weights),\n",
    "    components_distribution=tfd.MultivariateNormalDiag(\n",
    "        loc=means,\n",
    "        scale_diag=jnp.stack([jnp.ones(2) * b for b in betas], axis=0))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25ca38a1-3079-47dc-bf06-bc3045a7d96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The measurement is a linear projection of x with Gaussian noise.\n",
    "This means p(y|x) is also Gaussian.\n",
    "\"\"\"\n",
    "sigma = 0.2  # std. dev. of measurement noise\n",
    "a = np.array([-2., 1.7], dtype=np.float32)  # forward operator\n",
    "y = 0.3  # observation\n",
    "\n",
    "likelihood = lambda x: tfd.Normal(loc=jnp.dot(a, x), scale=sigma)\n",
    "\n",
    "def prob_y(y):\n",
    "  stds = jnp.sqrt(betas**2 * jnp.dot(a, a) + sigma**2)\n",
    "  dist = tfd.MixtureSameFamily(\n",
    "    mixture_distribution=tfd.Categorical(probs=weights),\n",
    "    components_distribution=tfd.MultivariateNormalDiag(\n",
    "        loc=jnp.einsum('j,bj->b', a, means)[:, None],\n",
    "        scale_diag=stds[:, None]))\n",
    "  return dist.prob(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed18f5cb-c440-4781-b374-f6634974c671",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "With the prior and likelihood defined, we also know the posterior.\n",
    "\"\"\"\n",
    "# Posterior p(x|y)\n",
    "@jax.vmap\n",
    "def posterior_prob(x, y):\n",
    "  py = prob_y(y)\n",
    "  px = prior.prob(x)\n",
    "  return likelihood(x).prob(y) * px / py\n",
    "\n",
    "# Parameters for plotting\n",
    "x1_range = (-0.8, 0.8)\n",
    "x2_range = (-0.8, 0.8)\n",
    "\n",
    "# Domain\n",
    "x1_grid, x2_grid = np.meshgrid(\n",
    "    np.linspace(*x1_range, 100), np.linspace(*x2_range, 100))\n",
    "x1_flattened = x1_grid.reshape(-1)\n",
    "x2_flattened = x2_grid.reshape(-1)\n",
    "X = np.stack([x1_flattened, x2_flattened], axis=-1)\n",
    "\n",
    "# True prior PDF\n",
    "prior_grid = prior.prob(X).reshape(100, 100)\n",
    "\n",
    "# True posterior PDF\n",
    "Y = np.full(X.shape[0], y).astype(np.float32)\n",
    "posterior_grid = posterior_prob(X, Y).reshape(100, 100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlim(*x1_range); ax.set_ylim(*x2_range)\n",
    "ax.set_xticks(x1_range); ax.set_yticks(x2_range)\n",
    "ax.contour(x1_grid, x2_grid, prior_grid, cmap='viridis', alpha=0.5)\n",
    "ax.contour(x1_grid, x2_grid, posterior_grid, cmap='magma')\n",
    "ax.plot(x1_flattened, y / a[1] - x1_flattened * (a[0] / a[1]),\n",
    "         linestyle='--', c='black')\n",
    "plt.title('true prior + posterior')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f8afb6-223e-4dc3-8299-044ec1c1f7ea",
   "metadata": {},
   "source": [
    "## DPI Posterior Sampling\n",
    "\n",
    "The DPI optimization objective is\n",
    "\\begin{align*}\n",
    "\\phi^* &= \\arg\\min_\\phi D_{\\text{KL}}(q_\\phi\\lVert p(\\cdot\\mid y) \\\\\n",
    "&= \\arg\\min_\\phi \\mathbb{E}_{\\mathbf{x}\\sim q_\\phi}[-\\log p(y\\mid\\mathbf{x})-\\log p(\\mathbf{x})+\\log q_\\phi(\\mathbf{x})],\n",
    "\\end{align*}\n",
    "where $\\phi$ are the parameters of a RealNVP normalizing flow. Of course, we could directly use the known prior $p(\\mathbf{x})$. However, for demonstration purposes, we will assume that we only have access to the time-dependent score function $\\nabla_\\mathbf{x} \\log p_t(\\mathbf{x})$, where\n",
    "\n",
    "\\begin{align*}\n",
    "p_t(\\mathbf{x})=\\sum_{k=1}^K\\mathcal{N}(\\mathbf{x}; \\alpha(t)\\mu_k, \\alpha(t)^2\\beta_k^2+\\beta(t)^2).\n",
    "\\end{align*}\n",
    "$\\alpha(t)$ and $\\beta(t)$ are scalars determined by the diffusion SDE. To calculate $\\log p(\\mathbf{x})$ with only the score function, we can use the probability flow ODE as proposed in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37434af9-992f-4466-82e2-456e94a0b4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The SDE dictates how the prior distribution changes over diffusion time\n",
    "and hence the probability flow ODE used to evaluate logp(x).\n",
    "\"\"\"\n",
    "sde = sde_lib.VPSDE()\n",
    "t0_eps = 1e-12  # smallest time for numerical stability\n",
    "\n",
    "def marginal_dist_params(t: float):\n",
    "  \"\"\"The mean coefficient and std. dev. of the marginal distribution at t.\"\"\"\n",
    "  all_ones = jnp.ones((1, 1))\n",
    "  t_batch = jnp.ones(1) * t\n",
    "  mean, std = sde.marginal_prob(all_ones, t_batch)\n",
    "  alpha_t = mean[0][0]\n",
    "  beta_t = std[0]\n",
    "  return alpha_t, beta_t\n",
    "\n",
    "def score_fn(x, t_batch):\n",
    "  def _unnormalized_log_prob(data, t):\n",
    "    alpha_t, beta_t = marginal_dist_params(t)\n",
    "    var_t = alpha_t**2 * betas**2 + beta_t**2\n",
    "    means_t = alpha_t * means\n",
    "    norm2 = jnp.linalg.norm(data - means_t, axis=-1)**2\n",
    "    return jax.scipy.special.logsumexp(-0.5 * norm2 / var_t)\n",
    "  return jax.vmap(jax.grad(lambda xi, ti: _unnormalized_log_prob(xi, ti)))(x, t_batch)\n",
    "\n",
    "# `prob_flow` is a wrapper for the probability flow ODE.\n",
    "prob_flow = probability_flow.ProbabilityFlow(\n",
    "  sde=sde,\n",
    "  score_fn=score_fn,\n",
    "  solver=diffrax.Dopri5(),\n",
    "  stepsize_controller=diffrax.PIDController(rtol=1e-3, atol=1e-5),\n",
    "  adjoint=diffrax.RecursiveCheckpointAdjoint(),\n",
    "  n_trace_estimates=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79314d22-c917-4564-a288-6b9fd25cf56d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Here we set up the RealNVP that we wish to optimize.\n",
    "\"\"\"\n",
    "batch_size = 2560\n",
    "learning_rate = 1e-5\n",
    "\n",
    "dim = 2  # data dimensionality (2 in this case)\n",
    "n_flow = 8  # number flow layers (each layer has 2 affine-coupling layers)\n",
    "\n",
    "orders, reverse_orders = realnvp_model.get_orders(dim, n_flow)\n",
    "model = realnvp_model.RealNVP(\n",
    "    out_dim=dim,\n",
    "    n_flow=n_flow,\n",
    "    orders=orders,\n",
    "    reverse_orders=reverse_orders,\n",
    "    seqfrac=1/128,\n",
    "    include_softplus=False,\n",
    "    init_softplus_log_scale=1,\n",
    "    train=True)\n",
    "\n",
    "# Initialize params and model state.\n",
    "init_rng = jax.random.PRNGKey(42)\n",
    "z = jax.random.normal(init_rng, (batch_size, dim))\n",
    "variables = model.init(init_rng, z, reverse=True)\n",
    "init_model_state, init_params = flax.core.pop(variables, 'params')\n",
    "\n",
    "# Initialize optimizer.\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "\n",
    "# Initialize training state.\n",
    "opt_state = optimizer.init(init_params)\n",
    "state = mutils.State(\n",
    "    step=0,\n",
    "    opt_state=opt_state,\n",
    "    params=init_params,\n",
    "    model_state=init_model_state,\n",
    "    entropy_weight=1,\n",
    "    rng=jax.random.PRNGKey(0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "863a3029-413d-477c-847a-cb7f7c7c830b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Here we define the optimization objective function and update step for the RealNVP.\n",
    "\"\"\"\n",
    "@jax.vmap\n",
    "def data_loss_fn(x):\n",
    "  return -likelihood(x).log_prob(Y)\n",
    "\n",
    "def prior_loss_fn(rng, x):\n",
    "  return -prob_flow.logp_fn(rng, x, t0=t0_eps, t1=sde.T, dt0=None)\n",
    "\n",
    "def get_sampling_fn(model, params, states, train=False):\n",
    "  def sample_fn(rng, shape):\n",
    "    # Sample latent.\n",
    "    z = jax.random.normal(rng, shape)\n",
    "\n",
    "    variables = {'params': params, **states}\n",
    "    if train:\n",
    "      (samples, logdet), new_states = model.apply(\n",
    "          variables, z, reverse=True, mutable=list(states.keys()))\n",
    "    else:\n",
    "      samples, logdet = model.apply(variables, z, reverse=True, mutable=False)\n",
    "      new_states = states\n",
    "    return (samples, logdet), new_states\n",
    "  return sample_fn\n",
    "\n",
    "def loss_fn(rng, params, model_state, lambda_entropy):\n",
    "  sample_fn = get_sampling_fn(model, params, model_state, train=True)\n",
    "\n",
    "  # Sample from the current state of the RealNVP.\n",
    "  rng, step_rng = jax.random.split(rng)\n",
    "  (samples, logdet), new_model_state = sample_fn(step_rng, (batch_size, dim))\n",
    "\n",
    "  # Compute loss across this batch of samples.\n",
    "  loss_data = data_loss_fn(samples)\n",
    "  rng, step_rng = jax.random.split(rng)\n",
    "  loss_prior = prior_loss_fn(step_rng, samples)\n",
    "  loss_entropy = -lambda_entropy * logdet\n",
    "\n",
    "  loss_data = jnp.mean(loss_data)\n",
    "  loss_prior = jnp.mean(loss_prior)\n",
    "  loss_entropy = jnp.mean(loss_entropy)\n",
    "  loss = loss_data + loss_prior + loss_entropy\n",
    "\n",
    "  return loss, (new_model_state, loss_data, loss_prior, loss_entropy, samples)\n",
    "\n",
    "def step_fn(rng, state):\n",
    "  val_and_grad_fn = jax.value_and_grad(loss_fn, argnums=1, has_aux=True)\n",
    "\n",
    "  params = state.params\n",
    "  model_state = state.model_state\n",
    "  opt_state = state.opt_state\n",
    "  (loss, (new_model_state, loss_data, loss_prior, loss_entropy, samples)), grad = val_and_grad_fn(\n",
    "      rng, params, model_state, lambda_entropy=state.entropy_weight)\n",
    "\n",
    "  # Apply updates.\n",
    "  updates, new_opt_state = optimizer.update(grad, opt_state, params)\n",
    "  new_params = optax.apply_updates(params, updates)\n",
    "\n",
    "  step = state.step + 1\n",
    "  new_state = state.replace(\n",
    "      step=step,\n",
    "      opt_state=new_opt_state,\n",
    "      params=new_params,\n",
    "      model_state=new_model_state,\n",
    "  )\n",
    "\n",
    "  return new_state, (loss, loss_data, loss_prior, loss_entropy), samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a695000-10ea-4d9d-bd73-9858e2771e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "We iteratively sample from the RealNVP, estimate its KL divergence to the target posterior,\n",
    "and update the RealNVP parameters with gradients to minimize the KL divergence.\n",
    "\"\"\"\n",
    "losses_total, losses_data, losses_prior, losses_entropy = [], [], [], []\n",
    "train_step = jax.jit(step_fn)\n",
    "\n",
    "init_step = state.step\n",
    "rng = state.rng\n",
    "for step in range(init_step, 12001):\n",
    "  rng, step_rng = jax.random.split(rng)\n",
    "  state, (loss, loss_data, loss_prior, loss_entropy), samples = train_step(\n",
    "      step_rng, state)\n",
    "\n",
    "  # Update losses.\n",
    "  losses_total.append(loss)\n",
    "  losses_data.append(loss_data)\n",
    "  losses_prior.append(loss_prior)\n",
    "  losses_entropy.append(loss_entropy)\n",
    "\n",
    "  if step % 10 == 0:\n",
    "    ipd.clear_output(wait=True)\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    fig.suptitle(f'step {step}', fontsize=20)\n",
    "\n",
    "    ax = axs[0]\n",
    "    ax.plot(losses_total); ax.set_title('loss', fontsize=18)\n",
    "\n",
    "    ax = axs[1]\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_xlim(*x1_range); ax.set_ylim(*x2_range)\n",
    "    ax.plot(x1_flattened, y / a[1] - x1_flattened * (a[0] / a[1]),\n",
    "            linestyle='--', c='black')\n",
    "    ax.contour(x1_grid, x2_grid, prior_grid, alpha=0.5, cmap='viridis')\n",
    "    ax.contour(x1_grid, x2_grid, posterior_grid, cmap='magma')\n",
    "    ax.scatter(\n",
    "        samples[:, 0], samples[:, 1],\n",
    "        s=1, alpha=0.5, label='DPI samples')\n",
    "    plt.title(f'$p(x|y={y})$')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ccd700-e4f8-4ea9-890a-d077b9b975c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
